where \(g\)  is the ReLU function [1]} that allows the model to produce sparse hidden output, which is more biologically plausible. Therefore, our model can be formulated in the same form as in NNRW with the hidden layer output matrix H and target matrix Y from Eq. (REF ). However, in contrast with NNRW, we estimate \(\beta \)  with a regularized Moore–Penrose inverse:
\(\beta ^{*} = (\textbf {H}^{T}\textbf {H} + \lambda \textbf {I})^{-1}\textbf {H}^{T}\textbf {Y}\) 
